BYOL and Non-Contrastive Self-Supervision Quiz

Answers and explanations appear as you take the quiz.

1. What makes BYOL non-contrastive?
   • It trains only with class labels
   • It learns without using negative image pairs
   • It does not use image augmentations
   • It compares each image with every other image
2. What are BYOL’s two networks called?
   • Online and target networks
   • Encoder and classifier only
   • Teacher and discriminator
   • Query and key databases
3. What does the online network learn to predict?
   • The image’s human class label
   • A negative pair’s representation
   • The target network’s raw pixel values only
   • The target network’s representation of another augmented view of the same image
4. How are target-network weights updated?
   • By ordinary gradient descent on the same loss
   • They remain random and frozen forever
   • By a slow-moving exponential average of online weights
   • By copying the classifier head each batch
5. Why does BYOL use an online predictor and stop-gradient on the target side?
   • They create an asymmetric learning setup that helps avoid representation collapse
   • They add negative examples
   • They turn the task into supervised classification
   • They make the target receive the same gradient
6. What happens to two views of the same image in BYOL?
   • They are assigned different class labels
   • They are encoded in the two networks and used to align learned representations
   • They are compared only to unrelated images
   • They are discarded after augmentation
7. Does BYOL require human class labels during its representation pretraining?
   • Yes, every view needs a class label
   • Only the target branch is labeled
   • No; it is a self-supervised image-representation method
   • It needs labels for negative pairs
8. What is a fair interpretation of BYOL’s benchmark results?
   • They prove labels are never useful
   • They guarantee superiority on all datasets
   • They show that all non-contrastive methods work identically
   • They demonstrate performance on specified evaluations, not a guarantee for every task